ReCoDe: Re-Conditioned Decoding for Faithful Controllable Generation

Controllable image generation conditions a diffusion model on spatial conditions, but only while the latent is being generated. The decoder that renders that latent never sees the condition, so structure is lost in the pixels: boundaries blur, thin lines break, and small objects fade. In this work, we propose to apply the condition twice, once to shape the latent and once in the decoder. Our method, dubbed Re-Conditioned Decoding (ReCoDe), denoises in pixel space under the original condition, and attaches to any conditioned generator without retraining it. Across three condition types, two datasets, and three controllers of a FLUX generator, ReCoDe overall outperforms condition-blind decoding of the fully denoised latent, improving pixel-space condition adherence on most metrics.